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目标跟踪算法综述

孟琭 杨旭

孟琭, 杨旭. 目标跟踪算法综述. 自动化学报, 2019, 45(7): 1244-1260. doi: 10.16383/j.aas.c180277
引用本文: 孟琭, 杨旭. 目标跟踪算法综述. 自动化学报, 2019, 45(7): 1244-1260. doi: 10.16383/j.aas.c180277
MENG Lu, YANG Xu. A Survey of Object Tracking Algorithms. ACTA AUTOMATICA SINICA, 2019, 45(7): 1244-1260. doi: 10.16383/j.aas.c180277
Citation: MENG Lu, YANG Xu. A Survey of Object Tracking Algorithms. ACTA AUTOMATICA SINICA, 2019, 45(7): 1244-1260. doi: 10.16383/j.aas.c180277

目标跟踪算法综述

doi: 10.16383/j.aas.c180277
基金项目: 

国家自然科学基金 61101057

详细信息
    作者简介:

    杨旭  东北大学信息科学与工程学院硕士研究生.主要研究方向为图像处理.E-mail:13998346746@163.com

    通讯作者:

    孟琭  东北大学信息科学与工程学院副教授.主要研究方向为人工智能及图像处理.本文通信作者. E-mail:menglu@ise.neu.edu.cn

A Survey of Object Tracking Algorithms

Funds: 

National Natural Science Foundation of China 61101057

More Information
    Author Bio:

     Master student at the College of Information Science and Engineering, Northeastern University. His main research interest is image processing

    Corresponding author: MENG Lu  Associate professor at the College of Information Science and Engineering, Northeastern Universiy. His research interest covers artificial intelligence and image processing. Corresponding author of this paper
  • 摘要: 目标跟踪一直以来都是计算机视觉领域的关键问题,最近随着人工智能技术的飞速发展,运动目标跟踪问题得到了越来越多的关注.本文对主流目标跟踪算法进行了综述,首先,介绍了目标跟踪中常见的问题,并由时间顺序对目标跟踪算法进行了分类:早期的经典跟踪算法、基于核相关滤波的跟踪算法以及基于深度学习的跟踪算法.接下来,对每一类中经典的跟踪算法的原始版本和各种改进版本做了介绍、分析以及比较.最后,使用OTB-2013数据集对目标跟踪算法进行测试,并对结果进行分析,得出了以下结论:1)相比于光流法、Kalman、Meanshift等传统算法,相关滤波类算法跟踪速度更快,深度学习类方法精度高.2)具有多特征融合以及深度特征的追踪器在跟踪精度方面的效果更好.3)使用强大的分类器是实现良好跟踪的基础.4)尺度的自适应以及模型的更新机制也影响着跟踪的精度.
    1)  本文责任编委 桑农
  • 图  1  Meanshift跟踪原理图

    Fig.  1  The tracking schematic of Meanshift

    图  2  Camshift算法流程图

    Fig.  2  Camshift algorithm flow chart

    图  3  核跟踪算法改进结构图

    Fig.  3  The improved structure diagram of kernel tracking algorithm

    图  4  相关滤波类算法发展方向

    Fig.  4  Development direction of correlation filters algorithm

    图  5  SAMF算法原理示意图

    Fig.  5  Schematic diagram of SAMF algorithm

    图  6  RPAC算法原理示意图

    Fig.  6  Schematic diagram of RPAC algorithm

    图  7  SRDCF空间正则化示意图[62]

    Fig.  7  SRDCF space regularization diagram[62]

    图  8  LMCF模型在线检测示意图[64]

    Fig.  8  LMCF model online detection diagram[64]

    图  9  50种目标跟踪算法在数据集OTB-2013上的总体性能对比, 这里只显示了排名前30的算法

    Fig.  9  The overall performance comparison of 50 object tracking algorithms in the data set OTB-2013, only the top 30 algorithms are shown here

    图  10  50种目标跟踪算法, 在数据集OTB-2013中11种属性下的成功率曲线

    Fig.  10  50 object tracking algorithms, success rate curves under 11 attributes in data set OTB-2013

    表  1  各种目标跟踪算法的速度比较

    Table  1  Speed comparison of various object tracking algorithms

    基于相关滤波 AUC FPS 基于深度学习 AUC FPS
    MCPF[83] 0.677 0.5 VITAL[78] 0.710 1.5
    BACF[15] 0.645 35 ECO[19] 0.709 6
    LMCF[64] 0.628 85 SANet[81] 0.677 1
    LCT[65] 0.628 27 MDNet[80] 0.670 1
    SAMF[16] 0.597 7 C-COT[72] 0.659 0.3
    DSST[50] 0.554 24 ADNet[84] 0.659 3
    KCF[14] 0.551 172 HDT[85] 0.654 10
    CSK[13] 0.398 368 SRDCFdecon[63] 0.653 1
    MOSSE[12] 0.357 669 CF2[66] 0.562 11
    ECO-HC[19] 0.652 20 DeepLMCF[64] 0.646 8
    DeepSRDCF[62] 0.641 0.3
    SiamFC[73] 0.612 58
    DRT[63] 0.581 0.4
    下载: 导出CSV
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出版历程
  • 收稿日期:  2018-05-04
  • 录用日期:  2018-07-16
  • 刊出日期:  2019-07-20

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